






"""simple docstring"""





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE   :							str          =		'''▁'''

_SCREAMING_SNAKE_CASE   :							Any          =		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	List[str]		=					BertGenerationTokenizer
    SCREAMING_SNAKE_CASE		:	Tuple		=					False
    SCREAMING_SNAKE_CASE		:	Tuple		=					True






    def   UpperCamelCase			(			self	:				Tuple   )	->						str:
      super().setUp()

      lowerCamelCase_       	=		BertGenerationTokenizer(__SCREAMING_SNAKE_CASE			,		keep_accents=__SCREAMING_SNAKE_CASE   )
      tokenizer.save_pretrained(self.tmpdirname   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		'<s>'
      lowerCamelCase_       	=		1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      lowerCamelCase_       	=		list(self.get_tokenizer().get_vocab().keys()   )

      self.assertEqual(vocab_keys[0]			,		'<unk>'   )
      self.assertEqual(vocab_keys[1]			,		'<s>'   )
      self.assertEqual(vocab_keys[-1]			,		'<pad>'   )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		1002   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Optional[int]:
      self.assertEqual(self.get_tokenizer().vocab_size			,		1000   )






    def   UpperCamelCase			(			self	:				Dict   )	->						List[str]:
      lowerCamelCase_       	=		BertGenerationTokenizer(__SCREAMING_SNAKE_CASE			,		keep_accents=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.tokenize('This is a test'   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		['▁This', '▁is', '▁a', '▁t', 'est']   )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		[285, 46, 10, 170, 382]			,		)

      lowerCamelCase_       	=		tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE			,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]			,		)
      lowerCamelCase_       	=		tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE			,		[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,		)

      lowerCamelCase_       	=		tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE			,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]			,		)






    @cached_property
    def   UpperCamelCase			(			self	:				Tuple   )	->						int:
      return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'   )






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[int]:
      lowerCamelCase_       	=		'Hello World!'
      lowerCamelCase_       	=		[18536, 2260, 101]

      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE   )   )






    @slow
    def   UpperCamelCase			(			self	:				str   )	->						Union[str, Any]:
      lowerCamelCase_       	=		(
          'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
          ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
      )
      lowerCamelCase_       	=		[
          871,
          419,
          358,
          946,
          991,
          2521,
          452,
          358,
          1357,
          387,
          7751,
          3536,
          112,
          985,
          456,
          126,
          865,
          938,
          5400,
          5734,
          458,
          1368,
          467,
          786,
          2462,
          5246,
          1159,
          633,
          865,
          4519,
          457,
          582,
          852,
          2557,
          427,
          916,
          508,
          405,
          34324,
          497,
          391,
          408,
          11342,
          1244,
          385,
          100,
          938,
          985,
          456,
          574,
          362,
          12597,
          3200,
          3129,
          1172,
      ]

      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE   )   )






    @require_torch
    @slow
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      import torch

      from transformers import BertGenerationConfig, BertGenerationEncoder

      # Build sequence
      lowerCamelCase_       	=		list(self.big_tokenizer.get_vocab().keys()   )[:10]
      lowerCamelCase_       	=		' '.join(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE			,		return_tensors='pt'			,		return_token_type_ids=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.big_tokenizer.batch_encode_plus(
          [sequence + ' ' + sequence]			,		return_tensors='pt'			,		return_token_type_ids=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		BertGenerationConfig()
      lowerCamelCase_       	=		BertGenerationEncoder(__SCREAMING_SNAKE_CASE   )

      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
        model(**__SCREAMING_SNAKE_CASE   )
        model(**__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Dict:
      # fmt: off
      lowerCamelCase_       	=		{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__SCREAMING_SNAKE_CASE			,		model_name='google/bert_for_seq_generation_L-24_bbc_encoder'			,		revision='c817d1fd1be2ffa69431227a1fe320544943d4db'			,		)












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




